This paper aims to understand why firms engage with their suppliers to collaborate for sustainability. For this purpose, we use the Carbon Disclosure Project (CDP) Supply Chain dataset and apply the Structural Topic Model to: 1) identify the topics discussed in an open-ended question related to climate-related supplier engagement and, 2) estimate the differences in the discussion of such topics between CDP members and non-members, respectively focal firms and first-tier suppliers. The analysis highlights that the two prominent reasons why firms engage with their suppliers relate to several aspects of the supply chain management, and the services and good transportation efficiency. It is further noted that first-tier suppliers do not possess established capabilities and, therefore, are still improving their processes. On the contrary, focal firms have more structured capabilities so to manage supplier engagement for information collection. This study demonstrates how big data and machine learning methods can be applied to analyse unstructured textual data from traditional surveys.

Salvatore, C., Madonna, A., Bianchi, A., Boffelli, A., Kalchschmidt, M. (2022). Collaborate for what: a structural topic model analysis on CDP data. In 4th International Conference on Advanced Research Methods and Analytics (CARMA2022) (pp.139-146). Valencia : Editorial Universitat Politècnica de València [10.4995/CARMA2022.2022.15074].

Collaborate for what: a structural topic model analysis on CDP data

Salvatore, C;
2022

Abstract

This paper aims to understand why firms engage with their suppliers to collaborate for sustainability. For this purpose, we use the Carbon Disclosure Project (CDP) Supply Chain dataset and apply the Structural Topic Model to: 1) identify the topics discussed in an open-ended question related to climate-related supplier engagement and, 2) estimate the differences in the discussion of such topics between CDP members and non-members, respectively focal firms and first-tier suppliers. The analysis highlights that the two prominent reasons why firms engage with their suppliers relate to several aspects of the supply chain management, and the services and good transportation efficiency. It is further noted that first-tier suppliers do not possess established capabilities and, therefore, are still improving their processes. On the contrary, focal firms have more structured capabilities so to manage supplier engagement for information collection. This study demonstrates how big data and machine learning methods can be applied to analyse unstructured textual data from traditional surveys.
paper
sustainable supply chain management; carbon disclosure project;supplier collaboration; structural topic model; text mining
English
4th International Conference on Advanced Research Methods and Analytics (CARMA2022)
2022
Domenech, J; Vicente, MR
4th International Conference on Advanced Research Methods and Analytics (CARMA2022)
978-84-1396-018-0
2022
139
146
https://carmaconf.org/wp-content/uploads/pdfs/15074.pdf
none
Salvatore, C., Madonna, A., Bianchi, A., Boffelli, A., Kalchschmidt, M. (2022). Collaborate for what: a structural topic model analysis on CDP data. In 4th International Conference on Advanced Research Methods and Analytics (CARMA2022) (pp.139-146). Valencia : Editorial Universitat Politècnica de València [10.4995/CARMA2022.2022.15074].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/402020
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
Social impact